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浏览/检索结果: 共10条，第1-10条 帮助 限定条件 专题：兰州理工大学    第一署名单位    第一作者单位    通讯作者单位     已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 Membrane fouling diagnosis of membrane components based on multi-feature information fusion 期刊论文 JOURNAL OF MEMBRANE SCIENCE, 2022, 卷号: 657 作者:  Shi, Yaoke;  Wang, Zhiwen;  Du, Xianjun;  Gong, Bin;  Lu, Yanrong 收藏  |  浏览/下载：23/0  |  提交时间：2022/07/19 MBR  CBAM-MUL-CNN  Feature fusion  Attention mechanism  Membrane fouling diagnosis   Research on the membrane fouling diagnosis of MBR membrane module based on ECA-CNN 期刊论文 JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING, 2022, 卷号: 10, 期号: 3 作者:  Shi, Yaoke;  Wang, Zhiwen;  Du, Xianjun;  Ling, Guobi;  Jia, Wenchao 收藏  |  浏览/下载：30/0  |  提交时间：2022/06/21 Membrane fouling  Convolutional neural network  Attention mechanism  Feature extraction  Fault diagnosis   Global attention network for collaborative saliency detection 期刊论文 International Journal of Machine Learning and Cybernetics, 2022 作者:  Li, Ce;  Xuan, Shuxing;  Liu, Fenghua;  Chang, Enbing;  Wu, Hailei 收藏  |  浏览/下载：20/0  |  提交时间：2022/07/20 Image processing  Attention  Co saliencies  Collaborative correlation  Feature enhancement  Global context  Global informations  Individual features  Saliency detection  Single images  Spatial attention   Deep Learning-Based Framework for the Detection of Cyberattack Using Feature Engineering 期刊论文 SECURITY AND COMMUNICATION NETWORKS, 2021, 卷号: 2021 作者:  Akhtar, Muhammad Shoaib;  Feng, Tao 收藏  |  浏览/下载：14/0  |  提交时间：2022/03/01 Research on Classification of COVID-19 Chest X-Ray Image Modal Feature Fusion Based on Deep Learning 期刊论文 JOURNAL OF HEALTHCARE ENGINEERING, 2021, 卷号: 2021 作者:  Ji, Dongsheng;  Zhang, Zhujun;  Zhao, Yanzhong;  Zhao, Qianchuan 收藏  |  浏览/下载：18/0  |  提交时间：2021/10/14 Fault Diagnosis Method for Rolling Bearing under Variable Working Conditions Using Improved Residual Neural Network 期刊论文 Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University, 2020, 卷号: 54, 期号: 9, 页码: 23-31 作者:  Zhao, Xiaoqiang;  Liang, Haopeng 收藏  |  浏览/下载：17/0  |  提交时间：2020/11/14 Convolution  Failure analysis  Fault detection  Microphones  Neural networks  Time domain analysis  End-to-end connections  Fault diagnosis method  Feature information  Generalization ability  Learning efficiency  Neural network method  Time-varying characteristics  Working environment   Deformation and Refined Features Based Lesion Detection on Chest X-Ray 期刊论文 IEEE Access, 2020, 卷号: 8, 期号: 8, 页码: 14675-14689 作者:  Ce Li;  Dong Zhang;  Shaoyi Du;  Zhiqiang Tian 收藏  |  浏览/下载：14/0  |  提交时间：2020/11/11 Convolution  Convolutional neural networks  Deep neural networks  Deformation  Signal detection   A Butterfly Detection Algorithm Based on Transfer Learning and Deformable Convolution Deep Learning 期刊论文 Zidonghua Xuebao/Acta Automatica Sinica, 2019, 卷号: 45, 期号: 9, 页码: 1772-1782 作者:  Li, Ce;  Zhang, Dong;  Du, Shao-Yi;  Zhu, Zi-Zhong;  Jia, Sheng-Ze 收藏  |  浏览/下载：13/0  |  提交时间：2020/11/14 Classification (of information)  Convolution  Deep neural networks  Deformation  Feature extraction  Neural networks  Signal detection  Transfer learning  Classification accuracy  Convolution neural network  Convolutional model  Detection algorithm  Ecological environments  Hardware environment  Image of living butterfly  Multi-classification   Fine-Grained Vehicle Classification With Channel Max Pooling Modified CNNs 期刊论文 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2019, 卷号: 68, 期号: 4, 页码: 3224-3233 作者:  Ma, Zhanyu;  Chang, Dongliang;  Xie, Jiyang;  Ding, Yifeng;  Wen, Shaoguo 收藏  |  浏览/下载：4/0  |  提交时间：2019/11/15 Fine-grained  vehicle classification  convolutional neural network  channel pooling  feature extraction   Fine-Grained Vehicle Classification with Channel Max Pooling Modified CNNs 期刊论文 IEEE Transactions on Vehicular Technology, 2019, 卷号: 68, 期号: 4, 页码: 3224-3233 作者:  Ma, Zhanyu;  Chang, Dongliang;  Xie, Jiyang;  Ding, Yifeng;  Wen, Shaoguo 收藏  |  浏览/下载：14/0  |  提交时间：2020/11/14 Backpropagation algorithms  Convolution  Feature extraction  Motivation  Neural networks  Vehicles  channel pooling  Classification accuracy  Competitive performance  Convolutional neural network  Discriminative features  Fine grained  State-of-the-art methods  Vehicle classification